Quiz Google - Valid GCP-DE - Exam Data Engineer Dump

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Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Managing and optimizing solutions	20%-25%	- Managing resources and costs 1. Optimize storage and compute usage 2. Control operational costs 3. Tune query and pipeline performance - Reliability and scalability 1. Implement disaster recovery strategies 2. Design highly available systems 3. Scale data workloads efficiently
Topic 2: Ensuring solution quality	20%-25%	- Data quality management 1. Implement data governance 2. Validate and clean data 3. Maintain metadata and lineage - Security and compliance 1. Protect sensitive data 2. Implement encryption and compliance controls 3. Manage IAM and access control
Topic 3: Building and operationalizing data processing systems	28%-33%	- Building data processing systems 1. Implement streaming data systems 2. Use BigQuery, Dataflow, Pub/Sub, Dataproc and related services 3. Implement batch processing systems - Operationalizing systems 1. Manage logging and alerting 2. Monitor data pipelines and workloads 3. Implement fault tolerance and recovery
Topic 4: Designing data processing systems	22%-27%	- Designing data pipelines 1. Design orchestration workflows 2. Build ETL and ELT pipelines 3. Optimize pipeline performance and cost - Designing for data ingestion 1. Select appropriate storage systems 2. Design scalable and reliable ingestion architecture 3. Design batch and streaming ingestion solutions

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GCP-DE Dump Collection | Certification GCP-DE Questions

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Google Data Engineer Sample Questions (Q69-Q74):

NEW QUESTION # 69
By default, which of the following windowing behavior does Dataflow apply to unbounded data sets?

A. Single, Global Window
B. Windows at every 10 minutes
C. Windows at every 1 minute
D. Windows at every 100 MB of data

Answer: A

Explanation:
Dataflow's default windowing behavior is to assign all elements of a PCollection to a single, global window, even for unbounded PCollections Reference: https://cloud.google.com/dataflow/model/pcollection

NEW QUESTION # 70
Which of the following statements is NOT true regarding Bigtable access roles?

A. To give a user access to only one table in a project, grant the user the Bigtable Editor role for that table.
B. You can configure access control only at the project level.
C. Using IAM roles, you cannot give a user access to only one table in a project, rather than all tables in a project.
D. To give a user access to only one table in a project, you must configure access through your application.

Answer: A

Explanation:
For Cloud Bigtable, you can configure access control at the project level. For example, you can grant the ability to:
Read from, but not write to, any table within the project.
Read from and write to any table within the project, but not manage instances. Read from and write to any table within the project, and manage instances. Reference: https://cloud.google.com/bigtable/docs/access-control

NEW QUESTION # 71
You are developing an application on Google Cloud that will automatically generate subject labels for users' blog posts. You are under competitive pressure to add this feature quickly, and you have no additional developer resources. No one on your team has experience with machine learning. What should you do?

A. Call the Cloud Natural Language API from your applicatio
B. Process the generated Entity Analysis as labels.
C. Deploy the model using a KubernetesEngine cluste
D. Build and train a text classification model using TensorFlo
E. Call the Cloud Natural Language API from your applicatio
F. Process the generated Sentiment Analysis as labels.
G. Call the model from your application and process the results as labels.
H. Call the model from your application and process the results as labels.
I. Build and train a text classification model using TensorFlo
J. Deploy the model using Cloud Machine Learning Engin

Answer: B

NEW QUESTION # 72
Each analytics team in your organization is running BigQuery jobs in their own projects. You want to enable each team to monitor slot usage within their projects. What should you do?

A. Create an aggregated log export at the organization level, capture the BigQuery job execution logs, create a custom metric based on the totalSlotMs, and create a Stackdriver Monitoring dashboard based on the custom metric
B. Create a log export for each project, capture the BigQuery job execution logs, create a custom metric based on the totalSlotMs, and create a Stackdriver Monitoring dashboard based on the custom metric
C. Create a Stackdriver Monitoring dashboard based on the BigQuery metric slots/allocated_for_project
D. Create a Stackdriver Monitoring dashboard based on the BigQuery metric query/scanned_bytes

Answer: A

NEW QUESTION # 73
Which of the following job types are supported by Cloud Dataproc (select 3 answers)?

A. Pig
B. Hive
C. YARN
D. Spark

Answer: A,B,D

Explanation:
Cloud Dataproc provides out-of-the box and end-to-end support for many of the most popular job types, including Spark, Spark SQL, PySpark, MapReduce, Hive, and Pig jobs.
Reference: https://cloud.google.com/dataproc/docs/resources/faq#what_type_of_jobs_can_i_run

NEW QUESTION # 74
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